Spectral analysis of electronic circuits

ABSTRACT

A method of designing a filter to meet a set of specifications. The set of specifications is received, and a filter design is established. Analysis of the filter design is performed by: determining a part admittance matrix; determining a circuit admittance matrix based on the part admittance matrices; reducing interior nodes of the circuit admittance matrix; reducing algebraic nodes to transform the circuit admittance matrix into a Green&#39;s Function; evaluating the Green&#39;s Function to determine a circuit exterior node admittance matrix; and transforming the circuit exterior node admittance matrix to a circuit scattering matrix. The circuit scattering matrix is compared to the set of specifications to determine whether the filter design is satisfactory. When a determination is made that the design is not satisfactory, the filter design is modified and the process is repeated. When a determination is made that the design is satisfactory, a filter design description is output.

NOTICE OF COPYRIGHTS AND TRADE DRESS

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. This patent document may showand/or describe matter which is or may become trade dress of the owner.The copyright and trade dress owner has no objection to the facsimilereproduction by anyone of the patent disclosure as it appears in thePatent and Trademark Office patent files or records, but otherwisereserves all copyright and trade dress rights whatsoever.

RELATED APPLICATION INFORMATION

This patent claims the benefit of and priority as a continuation to U.S.patent application Ser. No. 16/442,290 filed on Jun. 14, 2019; which isa continuation-in-part of U.S. patent application Ser. No. 15/390,254filed on Dec. 23, 2016; which claims priority from provisionalapplication No. 62/339,445 filed on May 20, 2016, entitled “SPECTRALANALYSIS OF ELECTRONIC CIRCUITS”; each of which is hereby incorporatedby reference in its entirety.

BACKGROUND Field

This disclosure relates to spectral analysis of electronic circuits.

Description of the Related Art

Conventional electronic circuit spectral analysis calculates an exteriornode admittance spectrum one frequency at a time. The circuit admittanceis the sum of admittances of component parts connected to interior andexterior nodes. Interior node elimination (or Kron reduction) yields theexterior node admittance, from which the circuit behavior may bepredicted. This process is repeated by a loop over frequency points tocreate an admittance spectrum.

Due to the large number of frequency points typically analyzed, and thefact that in implementations of analysis discussed above, centralprocessing unit (“CPU”) time scales linearly in the number of frequencypoints. This may require extensive amounts of time and expense (as wellas processor resources) when designing and manufacturing microwavefilter circuits.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow chart of calculation of spectra in conventionalcircuit analysis using Kron reduction.

FIG. 2 shows a flow chart of a method of electronic circuit spectralanalysis by algebraic node transformation.

FIG. 3A shows a schematic diagram of a unit shunt capacitor

FIG. 3B shows a schematic diagram of a unit shunt resistor.

FIG. 3C shows a schematic diagram of a J inverter.

FIG. 3D shows a schematic diagram of an assembly of a shunt resistorusing a unit shunt resistor and two J inverters.

FIG. 3E shows a schematic diagram of an assembly of an infinitesimalshunt conductance and a unit shunt capacitor.

FIG. 3F shows a schematic diagram of an assembly of a shunt inductorfrom a J inverter and a unit shunt capacitor.

FIG. 3G shows a schematic diagram of an assembly of a shunt capacitorfrom two J inverters and a unit shunt capacitor.

FIG. 3H shows a schematic diagram of an assembly of a series resistorusing three unit J inverters and a shunt resistor.

FIG. 3I shows a schematic diagram of standard nodes of a seriesinductor.

FIG. 3J shows a schematic diagram of expansion to algebraic nodes of theseries inductor of FIG. 3I.

FIG. 4A shows a schematic diagram of standard nodes of a seriescapacitor.

FIG. 4B shows a schematic diagram of node expansion of the seriescapacitor of FIG. 4A.

FIG. 5 shows a schematic diagram of circuit nodes in a ModifiedButterworth Van Dyke (MBVD) model for an acoustic wave resonator.

FIG. 6 shows a schematic diagram of algebraic node expansion of the MBVDmodel of FIG. 5.

FIG. 7 shows a schematic diagram of a reduced MBVD model afterelimination of interior circuit nodes.

FIG. 8 shows a schematic diagram of the reduced MBVD model of FIG. 7after diagonalization of the Hurwitz matrix.

FIG. 9 shows a flow chart of calculation of spectra of electroniccircuits using diagonalization for small-size Hurwitz matrices.

FIG. 10 shows a flow chart of the calculation of spectra of electroniccircuits using linear algebra appropriate for intermediate-size Hurwitzmatrices.

FIG. 11 shows a flow chart of calculation of spectra of electroniccircuits using the Kernel Polynomial Method appropriate for large-sizedHurwitz matrices.

FIG. 12 shows an S matrix for a reconfigurable SAW filter circuitexample.

FIG. 13 is a graph of central processing unit (“CPU”) time versus numberof frequency points for various methods of calculating the S matrix ofthe reconfigurable SAW filter example of FIG. 12.

FIG. 14 shows an S matrix for a SAW duplexer example.

FIG. 15 is a graph of CPU time versus number of frequency points forvarious methods for calculating the S matrix of the SAW duplexer exampleof FIG. 14.

FIG. 16 shows poles and zeros of the SAW duplexer example of FIG. 14.

FIG. 17 shows a flow chart of a process for designing a SAW filter.

DETAILED DESCRIPTION

As discussed above, design and manufacture of microwave filters usingiterative frequency-by-frequency analysis via calculation of an exteriornode admittance spectrum may require extensive processor resources andtime, slowing and adding expense to the manufacturing process. Instead,the systems and methods discussed herein are directed to an improved,faster and more efficient process for spectral analysis and manufactureof electronic circuits and equivalent linear time invariant systems withparticular emphasis on spectral analysis and design of microwave filtersusing acoustic resonators. Specifically, some or all of the frequencydependent features are removed from earlier parts of the analysis andshifted to later calculations, reducing or eliminating the iterativefrequency-by-frequency process at earlier or intermediate steps. Thismay result in a very significant savings in processing load on the CPU.In the limit case of very large number of frequency points, the CPU timedependence is linear but with a much smaller coefficient than inimplementations not utilizing the systems and methods described herein.

While specific examples discussed subsequently include surface acousticwave (SAW) resonators, the spectral analysis method is applicable tofilters using other forms of acoustic or mechanical resonators includingbulk acoustic wave (BAR) resonators, film bulk acoustic wave (FBAR)resonators, thin-film bulk acoustic wave (TBAR) resonators, andmicroelectromechanical system (MEMS) resonators. Symbolic algebra can beused to transform equations into a form that is more efficient forcomputation or that provides greater insight. An algebraic nodetransformation of the admittance can improve the scaling of CPU timewith the number of frequency points. The nodal analysis of theadmittance is transformed by a process of algebraic node expansion andinterior node reduction. The transformed form for the exterior nodeadmittance is analogous to a Green's, or impulse response, function.This simplifies the frequency dependence of the admittance, whichenables orders of magnitude faster computation of spectral propertiessuch as the scattering matrix and pole zero analysis.

The algebraic node transformation method is applicable to lumpedcomponent circuits assembled from resistors, inductors and capacitors.It is also applicable to many other linear time invariant systems whichhave equivalent circuit analogues. It may be adapted to distributedelectronic components such as calculated by electromagnetic simulation.

The design and characterization of circuits such as filters, duplexers,and multiplexers can involve processes of synthesis, optimization andsurvey over extremely large numbers of possible circuit designs.Spectral analysis over thousands of frequency points and pole zeroanalysis can be rate limiting steps for this design process. The methodsdescribed herein accelerate the circuit design process by orders ofmagnitude, which can enable discovery of new types of circuits.

The method introduces algebraic node analysis, and matrixtransformations comprised, in part, of successive nodal expansions andinterior node reductions, to isolate the frequency dependence from theremaining computation of the spectral response of a circuit. Thetransformation from interior to algebraic node representations of theadmittance enables most of the calculation to be done one time only forall frequencies, thereby accelerating calculation for many frequencypoints.

According to this method, a frequency dependent external node admittancematrix is transformed into the Green's function form. The Fouriertransform propagates as the exponential of time multiplied by a complexsymmetric stable Hurwitz matrix. Frequency independent matrices requiredto form the Green's function are calculated before a final vectorizedcalculation of the frequency spectrum. Efficient linear algebra methodsdeveloped for Green's function evaluation in many disciplines, such asphysics and chemistry, may be adapted to the spectral analysis ofcircuits. The central processing unit (“CPU”) time scales sublinearly inthe number of frequency points when the method described herein isemployed. It approaches linear scaling at large number of frequencypoints, but with a much smaller coefficient than the conventionalmethod. Detailed comparisons depend on the particular circuit and themethod for Green's function evaluation.

For many applications, the method is orders of magnitude faster than theconventional spectral analysis method, especially if large numbers offrequency points are desired. Examples drawn from surface acoustic wavedevice design demonstrate approximately two orders of magnitude speedincrease for spectral analysis and pole zero analysis.

The algebraic node transformation method also provides a newcharacterization of the dynamical behavior of circuits that iscomplementary to other established methods. Features of analyticity,causality, positivity and sum rules may transcend particularapplications. They can be used as powerful constraints on circuit dataanalysis and modeling.

FIG. 1 shows a flow diagram for a conventional circuit spectral analysismethod.

At step 101, a filter design (or parts list) for a circuit is received.The admittance matrix of a circuit is assembled from the admittancematrices of its parts (or components) by connecting parts together atnodes. The filter design may be represented as an electronic circuitcomposed of interconnected circuit components consisting of resistiveand reactive (i.e. capacitor and inductor) circuit components. Thefilter design can be based on a set of specifications, which mayinclude, for example, specifications on the lower and upper frequenciesof a pass band, one or more stop bands, a maximum insertion loss overthe pass band, a minimum insertion loss over each stop band, a minimumreturn loss, and/or other requirements. The filter design can also bebased on a selected architecture, including a substrate configuration, apiezoelectric material and crystalline orientation, the number, type,and arrangement of components, and other manufacturing-relatedcharacteristics.

At step 102, an admittance matrix is determined for each part at onefrequency using a library of frequency-dependent functions. This methodincludes a library of functions to calculate an admittance matrix foreach type of part. Simple parts such as resistors, inductors andcapacitors have only exterior nodes. Parts that are subcircuits haveinterior nodes as well.

At step 103, a circuit admittance matrix can be determined at onefrequency by attaching exterior nodes of parts to circuit nodes.

At step 104, the circuit interior nodes are then eliminated (or Kronreduced) to determine a circuit exterior node admittance matrix at onefrequency, where the submatrices are functions of frequency.

At step 105, the calculations of step 102, step 103, and step 104 arelooped over a number of frequencies spanning an indicated frequencyspectrum, such that a circuit exterior node admittance is determined atall frequencies of interest at step 106.

At step 107, the circuit exterior node admittance is transformed into acircuit scattering matrix at all frequencies of interest. As discussedabove, the CPU time for the conventional algorithm scales linearly inthe number of frequency points.

In the algebraic node transformation method shown in FIG. 2 anddescribed further herein, the introduction of frequency dependence ispushed to a later step. For a modest number of frequency points thisremoves most of the dependence of the CPU time on the number offrequency points. This is a very significant saving in processing loadon the CPU. In the limit case of very large number of frequency points,the CPU time dependence is linear but with a much smaller coefficientthan the conventional method.

At step 201, a filter design (or parts list) for a circuit is received.

At step 202, admittance matrices with algebraic nodes are determined foreach part (or component) in the filter design (or parts list) using alibrary of frequency-independent functions. Here, an expanded admittancematrix is calculated for each type of part, which includes exterior andinterior circuit nodes, as well as algebraic nodes. The diagonalelements of the admittance matrix for algebraic nodes have a linearfrequency dependence. All other admittance matrix elements are frequencyindependent.

At step 203, the expanded circuit admittance with exterior, interior andalgebraic nodes is assembled by connecting exterior nodes of parts toexterior and interior circuit nodes. The expanded admittance for thecomplete circuit retains all the algebraic nodes of the simple parts andthe subcircuits that comprise the whole. The only frequency dependenceis linear in the diagonal elements of the expanded admittance matrix foralgebraic nodes. All other elements of the expanded circuit admittancematrix are frequency independent.

At step 204, the expanded circuit admittance is transformed into areduced circuit admittance matrix for only exterior and algebraic nodes.The interior circuit nodes are eliminated by Kron reduction. The reducedmatrix is the sum of a frequency-dependent diagonal matrix and afrequency-independent complex symmetric matrix. The algebraic nodesubmatrix of the frequency independent matrix satisfies the stabilityproperties of a Hurwitz matrix. Eigenvalues are either positive real orcomplex conjugate pairs with positive real parts. They describe theinternal dynamics of the system. External coupling is provided by thematrix elements between algebraic and exterior nodes.

At step 205, this admittance is further transformed by reduction of thealgebraic nodes. The resulting circuit admittance matrix for exteriorcircuit nodes has a Green's Function form with a spectral dependence onthe Hurwitz matrix. This form is similar mathematically to impulse orlinear response functions in other disciplines.

At step 206, the Green's Function is evaluated to determine a circuitexterior node admittance at all frequencies of interest. The submatricesin this expression are independent of frequency, so they only need to becalculated once. For modest numbers of frequency points, the CPU timerequired to evaluate the submatrices for all frequency points in thefast circuit spectral analysis method is comparable to the time requiredfor evaluation of submatrices for a single frequency point in theconventional method. A variety of existing numerical methods developedin other disciplines may be adapted to evaluate Green's functions.

At step 207, the circuit exterior node admittance is transformed into acircuit scattering matrix at all frequencies of interest.

Algebraic Node Expansion

In nodal analysis, a circuit is represented by a set of nodes withelectrical components connected between them. Each circuit node isassociated with a current I and voltage V. A set of nodes is associatedwith vectors of currents I and voltages V. By definition, the admittancematrix Y is the relation between currents and voltages, I=YV. Thecircuit obeys Kirchoff Current Law. Currents for ‘interior’ nodes arezero. Currents for ‘exterior’ nodes may be non-zero.

Algebraic nodes may be introduced by considering the following problem.Assume there are fundamental units of resistance R₀ and capacitance C₀.And assume there are elemental shunt components with these values shownin FIG. 3. The problem to be solved is to describe a way of assemblingall RLC circuits by connecting the unit components using only Jinverters.

The admittances for unit components are as follows.

For a unit shunt capacitor shown in FIG. 3A,

Y ₁ ^(C) =jωC _(u).

For a unit shunt resistor shown in FIG. 3B,

$Y_{1}^{R} = {\frac{1}{R_{u}}.}$

For a unit J inverter in FIG. 3C,

${Y_{2}^{J} = {\frac{j}{R_{u}}\begin{pmatrix}0 & J \\J & 0\end{pmatrix}}}.$

Shunt means the component is connected between an exterior node andground. J inverters are represented by a double line between twoexterior nodes.

The choice of scales is arbitrary. The unit of resistance may be an ohmΩ, or it may be the transmission line standard of 50Ω. The unit ofcapacitance may be a picofarad. R_(u)=1 and C_(u)=1 in the selectedunits. Using dimensional analysis, if required, the admittances unitscan be reintroduced at the end of a calculation.

FIG. 3D shows that a shunt resistor for any R may be assembled fromthese basic components by connecting a unit shunt resistor to two jinverters with J=√{square root over (R)} and J=1. A node shared by twoor more components, and not with the exterior, is an ‘interior’ node.The admittance is

$Y_{3}^{R} = {\begin{pmatrix}0 & j & 0 \\j & 0 & {j\sqrt{R}} \\0 & {j\sqrt{R}} & 1\end{pmatrix}.}$

There are two interior nodes.

Node elimination, or Kron reduction, is important to all that follows.To clarify, suppose there are two types of nodes, a and b. In blocksubmatrix form

${\begin{pmatrix}I_{a} \\I_{b}\end{pmatrix} = {\begin{pmatrix}Y_{aa} & Y_{ab} \\Y_{ba} & Y_{bb}\end{pmatrix} \times \begin{pmatrix}V_{a} \\V_{b}\end{pmatrix}}}.$

To eliminate all the b nodes, invoke Kirchoffs Current Law to solve forI_(b)=0. The result is

Y _(aa) ^(r) =Y _(aa) −Y _(ab) Y _(bb) ⁻¹ Y _(ba).

Here, the superscript r denotes reduced.

For the shunt resistor example, the first node is exterior and thesecond two are interior. Elimination of the third node yields

${Y_{2}^{R} = \begin{pmatrix}0 & j \\j & R\end{pmatrix}}.$

There is an impedance R on the second node. Reduce that to yield thefamiliar one node admittance, Y₁ ^(R)=1/R.

The two and three node admittances are obtained from the one nodeadmittance by ‘expansion’. The reverse process is ‘reduction’. All threeforms produce the same electrical behavior, so they are ‘equivalentcircuits’.

The unit shunt capacitor is the only frequency ω dependent elementalcomponent. Use of it requires complex variable analysis. An equivalentcircuit is needed to control mathematical singularities in the complex ωplane, especially how to close contour integrals. The solution shown inFIG. 3E puts each unit shunt capacitor in parallel with an infinitesimalshunt conductance ε or inverse resistance. The combined admittance isY₁=jω+ε.

The infinitesimal defines a limit, as in

${\lim\limits_{ɛ\rightarrow 0}\frac{1}{x \pm {jɛ}}} = {{P\frac{1}{x}} \mp {j\; {\pi\delta}\; {(x).}}}$

Here, δ(x) is a Dirac delta function, an infinitely sharp positivefunction with an integral equal to one. P indicates the Cauchy principalvalue in integrals. It relates real and imaginary parts of quantities tobe calculated. It assures the Fourier transforms are causal, which meansadmittances for passive circuits decay with increasing time. Losslesscomponents need this limiting procedure. For lossy components, resistorsmay replace ε to define complex ω plane contour integrals.

In the following, a unit shunt capacitor is implicitly in parallel withan infinitesimal conductance ε. Equivalently, co implicitly has aninfinitesimal imaginary part −jε. It is made explicit only where neededfor emphasis.

An equivalent circuit for a shunt inductor can be assembled from thesecomponents as shown FIG. 3F, by connecting the exterior node of a shuntunit capacitor and the inverter with J=1/√{square root over (L)}. Thetwo node expanded admittance of an inductor is

$Y_{2}^{L} = {{j\begin{pmatrix}0 & \frac{1}{\sqrt{L}} \\\frac{1}{\sqrt{L}} & \omega\end{pmatrix}}.}$

Elimination, or Kron reduction, of the algebraic node results in theusual one exterior node admittance for an inductor, Y₁ ^(L)=1jωL. Thatproves the expanded two node circuit expanded is an equivalent circuitfor an inductor.

The two node admittance puts the frequency ω and the inductor Lvariables on separate matrix elements. As shall be demonstrated, thisseparation enables linear algebra transformations that are advantageousfor calculating frequency sweeps, or the spectral response, of acircuit. For that reason, the new interior node of the shunt capacitoris labeled an ‘algebraic’ node.

An equivalent circuit for a shunt capacitor can be assembled as shown inFIG. 3G, by connecting two J inverters with a unit capacitor. One Jinverter has J=1 and the other has J=1/√{square root over (C)}. Thethree node expanded admittance of a capacitor is

$Y_{3}^{C} = {{j\begin{pmatrix}0 & 1 & 0 \\1 & 0 & \frac{1}{\sqrt{C}} \\0 & \frac{1}{\sqrt{C}} & \omega\end{pmatrix}}.}$

The first node is an exterior node, the second node is interior, and thethird node is algebraic. Elimination of the algebraic and interior nodesyields the familiar one exterior node admittance Y₁ ^(C)=jωC. The two Jinverters have enabled a scale transformation of the unit capacitor.

An equivalent circuit for a series component can be assembled as shownin FIG. 3H, by connecting the corresponding shunt component to a Tnetwork of three unit J inverters. The four node expanded admittance ofa series resistor is

$Y_{4}^{R} = {\begin{pmatrix}0 & 0 & j & 0 \\0 & 0 & {- j} & 0 \\j & {- j} & 0 & j \\0 & 0 & j & \frac{1}{R}\end{pmatrix}.}$

The first two nodes are exterior, and the second two are interior.Elimination of the interior nodes results in the standard two exteriornode admittance for a series resistor,

$Y_{2}^{R} = {\begin{pmatrix}\frac{1}{R} & {- \frac{1}{R}} \\{- \frac{1}{R}} & \frac{1}{R}\end{pmatrix}.}$

For the applications considered in this disclosure, the interior nodesmay be eliminated. However, there can be zeros on the diagonal elementsfor the interior nodes, which may result in division by zero during nodeelimination. This problem may be regularized by adding an infinitesimalshunt resistance or conductance at the node to be eliminated.

For example, elimination of the interior node for the shunt capacitor Y₃^(C) yields

$Y_{2}^{C} = {{j\; {\omega \begin{pmatrix}0 & 0 \\0 & 1\end{pmatrix}}} + {\frac{1}{ɛ}{\begin{pmatrix}1 & \frac{1}{\sqrt{C}} \\\frac{1}{\sqrt{C}} & \frac{1}{C}\end{pmatrix}.}}}$

This is a two node expanded admittance, with one exterior node and onealgebraic node.

Two equivalent ways of representing the one node admittance are

$Y_{1}^{C} = {{\frac{1}{\frac{1}{j\; \omega \; C} + ɛ}\text{;}\mspace{14mu} Y_{1}^{C}} = {{j\; \omega \; C} + {ɛ.}}}$

The purpose of infinitesimals is to track how to take a limit in complexvariable analysis. So the rules for infinitesimal math differ fromnormal math, in the same sense that rules for infinity math differ. Thatis, ε multiplied by something is ε times the sign of something, ε+ε=ε,ε×ε=ε, and so on. In the first equation, ω is implicitly ω−jε. The twoequations are equivalent in the sense of infinitesimal math.

The fully expanded admittance for a series inductor has five nodes, twoexterior, two interior and one algebraic. To regularize, add aninfinitesimal shunt conductance at the shunt inductor node, andeliminate interior nodes. The reduced admittance has two exterior nodesand one algebraic node,

$Y_{3}^{L} = {\begin{pmatrix}ɛ & {- ɛ} & {- \frac{j}{\sqrt{L}}} \\{- ɛ} & ɛ & \frac{j}{\sqrt{L}} \\{- \frac{j}{\sqrt{L}}} & \frac{j}{\sqrt{L}} & {j\; \omega}\end{pmatrix}.}$

The ε elements are negligible for numerical purposes and the ε→0 limitmay be taken.

The use of tiny resistors and conductances as regularization in complexvariable analysis differs from their use to regularize numericalcalculations. A computer has limited precision. Regularization by addingtiny resistances or conductances is helpful to dampen effects of randommachine errors. The scale for regularization corresponds to the squareroot of machine precision, approximately 10⁻⁸Ω for typical computers.Such numerical regularization is not taken to a zero limit. Too small aregularization can cause numerical calculations to go unstable resultingin, e.g., division by zero errors.

Equivalent circuits for series inductors and capacitors with internalnodes eliminated are illustrated in FIGS. 3I, 3J, 4A, and 4B. Thesefigures use a simplified symbol for the algebraic node as just a circlewith an ω on it.

FIG. 3I shows a schematic diagram of nodes of a series inductor 300, andFIG. 3J shows a schematic diagram of node expansion of the seriesinductor of FIG. 3I. Nodes 301 and 302 are exterior nodes. Mathematicalexpressions give the value of the elements. Node 303 is a frequencydependent algebraic node, which means that the diagonal admittanceY₃₃=jω. Inverters 305, 307 do not depend on ω. An inverter is a circuitelement that flips voltages to currents, and vice versa. Labels on theinverters represent values of the inverters.

FIG. 4A shows a schematic diagram of nodes of a series capacitor 400,and FIG. 4B shows a schematic diagram of node expansion of the seriescapacitor 400 of FIG. 4A. Nodes 401 and 402 are exterior nodes. Node 404is a frequency dependent algebraic node, and node 403 is a frequencyindependent algebraic node, such that Y₃₃=0. Labels on J-inverters, 405,406, and 407 represent values of the inverters.

As shown above, equivalent circuits for any RLC circuit can be assembledfrom the simple components in FIGS. 3A-3H. As they are connected in anetwork, exterior nodes become interior, and the algebraic nodes areunchanged. If not useful, interior nodes of any component may beeliminated, resulting in expanded admittances that have only exteriornodes and algebraic nodes.

Reduction of Algebraic Nodes to Green's Function Form

After reduction of all interior nodes, all RLC circuits have anadmittance expanded with algebraic nodes in the block matrix form

$Y = {{j\; {\omega \begin{pmatrix}0 & 0 \\0 & 1\end{pmatrix}}} + {\begin{pmatrix}Y_{ee} & Y_{ea} \\Y_{ae} & Y_{aa}\end{pmatrix}.}}$

A subscript ‘e’ denotes an exterior node, and a subscript ‘a’ denotes analgebraic node. Here 1 denotes a unit diagonal matrix in the space ofalgebraic nodes. All of the ω dependence is made explicit by theexpansion to algebraic nodes. The number of such nodes equals the sum ofcapacitors and inductors in the circuit.

Expansion of the admittance to algebraic nodes is a great simplificationof the frequency dependence of RLC circuits. All of the block matricesneed to be calculated only once for all frequencies. As shall be shown,this dramatically accelerates the spectral analysis of circuits.

To compute the admittance spectrum for exterior nodes, reduce allalgebraic nodes

${Y_{ee}^{r}(\omega)} = {Y_{ee} - {Y_{ea}\frac{1}{{\left( {{j\; \omega} + ɛ} \right)1} + Y_{aa}}{Y_{ae}.}}}$

Unless a matrix has an explicit (ω) dependence, it is frequencyindependent. Such matrices need to be calculated only once for acomplete spectral analysis. Here the 1 is a diagonal unit matrix in thespace of algebraic nodes.

Define the Laplace transform variable s≡jω. In order for the RLC circuitto be stable, the characteristic polynomial,

P(s)=det[(s+ε)1+Y _(aa)],

must satisfy the Hurwitz criterion. It must have real coefficients ofpowers of s. The roots are in the left hand complex s-plane. The sensures this condition is met even in the lossless circuit limit. Rootsare either negative real, or they come in complex conjugate pairs withnegative real parts. The roots are dynamical modes of the system.

To address spectral properties, it is better to work with H≡jY_(aa). TheFourier transform of the admittance evolves with time by multiplying bye^(jHt). H is a square symmetric matrix. It is real for losslesscircuits and complex for lossy circuits. A square complex matrix H maybe diagonalized with left and right eigenvector matrices W′ and V,respectively

HV=VE;W′H=EW′.

E is a diagonal matrix of eigenvalues corresponding to resonantfrequencies of the circuit. V is a matrix of eigenvectors, which arelinear superpositions of the algebraic nodes.

Symmetric means that the non-conjugate transpose H^(T)=H. A diagonalmatrix satisfies E^(T)=E. Then a sequence of substitutions leads to

V ^(T) H ^(T) =E ^(T) V ^(T) →V ^(T) H=EV ^(T) →W′=V ^(T)

Multiply the left hand side by V^(T) and the right hand side of thesecond by V. The result is

V ^(T) VE=V ^(T) HV=EV ^(TV).

Inasmuch as this shows K≡V^(T) V commutes with a non-unit diagonalmatrix, it must also be diagonal. It follows that

$V^{- 1} = {{\frac{1}{K}V^{T}\text{;}\mspace{14mu} H} = {{VEV}^{- 1}\text{;}}}$

In the limit of lossless circuits, H is real symmetric, K→1,eigenvectors are orthonormal, and eigenvalues are real.

The Hurwitz stability property requires real parts to be paired,E_(k)=±w_(k)+jΓ_(k), or to be pure imaginary E_(k)=jΓ_(k). This symmetryhelps ensure the admittance obeys Y(−ω)=Y(ω)*; that is, the admittanceis Fourier transform to a real causal convolution function of time.

The inverse matrix has a representation in terms of eigenvalues andeigenvectors,

${{G_{ij}(\omega)} \equiv \left( \frac{1}{{\left( {{j\; \omega} + ɛ} \right)1} + Y_{aa}} \right)_{ij}} = {\sum\limits_{k}{V_{ik}\frac{1}{{j\left( {\omega - w_{k}} \right)} + ɛ + \Gamma_{k}}V_{kj}^{- 1}}}$

The real part is positive definite and satisfies a frequency integralsum rule,

$1 = {\frac{2}{\pi}{\int_{0}^{\infty}{\Re \; {G(\omega)}d\; {\omega.}}}}$

Here 1 is a diagonal unit matrix in the space of algebraic nodes.

Real and imaginary parts are not independent. They are related by Cauchyprincipal value integrals, as in

${\; {G(\omega)}} = {\frac{2\; \omega}{\pi}P{\int_{0}^{\infty}{\frac{\Re \; {G\left( \omega^{\prime} \right)}}{\omega^{\prime \; 2} - \omega^{2}}d\; {\omega^{\prime}.}}}}$

Here P indicates to take the principal value integral. They are aconsequence of analyticity in the lower half ω plane. That is, alleigenvalues, and therefore poles of G, are in the upper half planecorresponding to decaying modes in the Fourier transform time variable.

Then the exterior node spectral admittance is,

Y _(ee)′(ω)=Y _(ee) −Y _(ea) G(ω)Y _(ae) ^(T).

It is the sum of a real frequency independent term and a projection ofG(w) from algebraic nodes to exterior nodes using a non-conjugatetranspose inner product.

Modified Butterworth Van Dyke Model Example

FIG. 5 shows a Modified Butterworth Van Dyke (MBVD) model 500 for anacoustic wave resonator. It is used, for example, in the design of radiofrequency filters for cell phones. FIG. 5 shows a diagram of circuitnodes and parts in a MBVD model 500. The parts comprise one inductor L,two capacitors C and C₀, and two resistors R and R₀. C₀ is called thestatic capacitance. The R, L, C in series is called the motionalresonator. Conventional nodal analysis yields 5 circuit nodes. Nodes 501and 502 are exterior circuit nodes, and nodes 503, 504, and 505 areinterior circuit nodes.

FIG. 6 is a schematic diagram of the 10 node expanded admittance for theMBVD 500 of FIG. 5. The algebraic node expansion yields afrequency-dependent algebraic node 606 for the inductor,frequency-independent and frequency-dependent pairs of algebraic nodes607, 608 for C₀, nodes 609, 610 for C, and inverters 611, 612, 613, 614,615, 616, 617, 618, and 619. The labels on the inverters are the valueof the inverter. The labels are mathematical expressions for thecomponent Y/j values.

Connecting an MBVD model as a subcircuit in a filter circuit requiresuses only the exterior e circuit nodes. Calculating the frequencydependence of the MBVD circuit uses only the algebraic nodes. Theinterior circuit nodes 603, 604, and 605 and the algebraic nodes 607 and609 of the two capacitors C and C₀, respectively, may be reduced. TheKron reduction procedure is to set the currents for these nodes to zeroand to solve for the relation of currents and voltages of the remainingnodes. The calculation proceeds in the order of nodes that do not havezeros on the diagonal. The result is a five-node reduced admittancematrix for the MBVD that has only two exterior circuit nodes and threefrequency-dependent algebraic nodes, represented as:

$Y_{5}^{BVD} = {{j\; {\omega \begin{pmatrix}0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 \\0 & 0 & 1 & 0 & 0 \\0 & 0 & 0 & 1 & 0 \\0 & 0 & 0 & 0 & 1\end{pmatrix}}} + {\begin{pmatrix}\frac{1}{R_{0}} & {- \frac{1}{R_{0}}} & 0 & {- \frac{j}{\sqrt{L}}} & {- \frac{1}{R_{0}\sqrt{C_{0}}}} \\{- \frac{1}{R_{0}}} & \frac{1}{R_{0}} & 0 & \frac{j}{\sqrt{L}} & \frac{1}{R_{0}\sqrt{C_{0}}} \\0 & 0 & 0 & \frac{j}{\sqrt{LC}} & 0 \\{- \frac{j}{\sqrt{L}}} & \frac{j}{\sqrt{L}} & \frac{j}{\sqrt{LC}} & \frac{R}{L} & 0 \\{- \frac{1}{R_{0}\sqrt{C_{0}}}} & \frac{1}{R_{0}\sqrt{C_{0}}} & 0 & 0 & \frac{1}{R_{0}C_{0}}\end{pmatrix}.}}$

The reduced admittance of the MBVD has the characteristic form of thesum of frequency times a diagonal unit matrix and a frequencyindependent complex symmetric matrix.

FIG. 7 shows a schematic diagram of the reduced admittance matrix of anMBVD 700, corresponding to the representation of the reduced admittancematrix of the MBVD above. The labels are mathematical expressions forthe component values of the admittance. A label on a single node denotesthat it is the value of the diagonal element. Labels on inverters 721,722, 723, 724, and 725 are the values of the inverter. The reductionprocess has added frequency independent values to the ω on the diagonalfor frequency dependent nodes. Node 708, which corresponds to R₀ and C₀for the static capacitance, is directly connected to exterior nodes 701and 702. The resistor contributes to the direct term {tilde over(Y)}_(ee). For the motional branch resonator corresponding to the seriesR, L and C in FIG. 5, only node 706 connects to the exterior nodes. Node710 connects only to node 706.

The H is j times the algebraic node submatrix of thefrequency-independent part of the reduced admittance. The eigenvalues ofH are

$E_{\pm} = {{{{\pm \sqrt{\frac{1}{LC} - \left( \frac{R}{2\; L} \right)^{2}}} + \frac{j\; R}{2\; L}} \equiv {{\pm \frac{1}{\sqrt{LC}}}e^{{\pm j}\; \Theta}\text{;}\mspace{14mu} E_{3}}} = \frac{j}{R_{0}C_{0}}}$

The first two eigenvalues are the resonant frequencies of the motionalresonator damped by the resistor. The third eigenvalue is theexponential decay rate for the static capacitance R₀, C₀ part of thecircuit. The right eigenvectors for an MBVD can be expressed in terms ofloss angle θ:

$V = \begin{pmatrix}\frac{1}{\sqrt{1 + e^{2\; j\; \Theta}}} & \frac{1}{\sqrt{1 + e^{{- 2}\; j\; \Theta}}} & 0 \\\frac{- e^{j\; \Theta}}{\sqrt{1 + e^{2\; j\; \Theta}}} & \frac{e^{{- j}\; \Theta}}{\sqrt{1 + e^{{- 2}\; j\; \Theta}}} & 0 \\0 & 0 & 1\end{pmatrix}$

The eigennodes are linear superpositions of the original algebraicnodes. For a lossless MBVD, the angle θ is zero, and the eigennodes areorthonormal. For lossy circuits, the eigennodes in general are notorthonormal.

FIG. 8 shows a schematic diagram of the admittance matrix afterdiagonalization of H 800. The algebraic parts nodes 706 and 710 of FIG.7 have been replaced by algebraic eigennodes 811 and 812. These arelinear combinations of algebraic parts nodes that diagonalize H. Theadmittance matrix after diagonalization becomes a transversal array.That is, the algebraic eigennodes are only connected to exterior nodesby J-inverters 821, 822, 823, 824, and 825. Node 805 is already aneigennode. Eigennodes do not connect to each other.

The MBVD is only one example of the nodal analysis of the algebraic nodetransformation method. An infinite number of other circuits comprised ofR, L, and C parts may be analyzed using algebraic node expansion andKron reduction. They will show similar characteristics.

Numerical Evaluation of Greens Function

FIG. 9 shows a flow diagram of one method of evaluation of Green'sFunction to determine a circuit exterior node admittance at allfrequencies, as shown in step 206 of FIG. 2. This uses Hurwitz matrixdiagonalization and vectorization. The method of FIG. 9 is most suitablefor small-size Hurwitz matrices.

At step 901, the Hurwitz matrix H, which as discussed above is j timesthe algebraic node submatrix of admittance, is diagonalized usingefficient linear algebra techniques. Most of the CPU time is spent inthe diagonalization. The matrix dimension N is the sum of the number ofinductors and the number of capacitors. The CPU time for diagonalizingnon-sparse matrices scales as N³. The memory scales as N².Diagonalization is fast for small circuits. The exterior node admittanceat one frequency is calculated as a sum over eigennodes of the Hurwitzmatrix.

At step 902, the calculation over many frequency points is vectorized,i.e. a style of computer programming where operations are applied towhole arrays instead of individual elements. Multiple frequencies arecalculated almost as quickly as a single frequency. A sweep of, forexample, 1000 frequency points using the fast circuit spectral analysismethod requires CPU time comparable to calculating a single frequencypoint in the conventional method.

Here, the Hurwitz matrix is diagonalized once independent of the numberof frequency points. The sweep over frequencies may be vectorized andrequires negligible CPU time.

At step 207, the method continues, as in FIG. 2, where the circuitexterior node admittance is transformed to a circuit scattering matrixat all frequencies of interest. For filter, duplexer, and multiplexersapplications, the S matrix is a suitable design characterization. Forother kinds of circuits and applications, equivalent matrixrepresentations for circuit performance (e.g. admittance, impedance, andABCD) may be more appropriate. For a two port device, an S-matrix is a2×2 matrix containing four S-parameters, or scattering parameters. AnS-matrix is a convention used to describe the performance of linearelectrical networks. S(1,2) is the transfer function from port 1 to port2 of the electronic circuit. S(1,2) is essentially the “insertion loss”of the electronic circuit with a change in numeric sign (e.g. S(1,2)=−3dB is equivalent to an insertion loss of 3 dB). Similarly S(2,1) is thetransfer function from port 2 to port 1 of the electronic circuit.S(1,1) is the reflection at port 1 and S(2,2) is the reflection at port2. For a three port device, such as a duplexer, the S-matrix is a 3×3matrix. And so on.

For large circuits, the CPU time and memory required to diagonalize theHurwitz matrix may be too large.

FIG. 10 shows a flow diagram of another method of evaluation of Green'sFunction using linear algebra that may be more appropriate forintermediate size Hurwitz matrices. The goal is the same, to determine acircuit exterior node admittance at all frequencies, as shown in step206 of FIG. 2.

At step 1001, Green's Function is evaluated by linear algebra onefrequency at a time over a frequency spectrum of interest. Linearalgebra methods, such as LU decomposition, are used to calculate theeffect of (ω−{tilde over (H)})⁻¹. The linear algebra is done onefrequency point at a time, rather than array processing (or vectorizing)the frequency dependence. This will be more efficient than theconventional circuit spectral analysis method, because the work ofcalculating the required submatrices (such as the Hurwitz matrix and theexterior to algebraic node admittance) need only be done once regardlessof the number of frequency points. However, this frequency loop is noteasily vectorized, and the CPU time will scale linearly in the number offrequency points.

FIG. 11 shows a flow diagram of yet another method of evaluation ofGreen's Function to determine a circuit exterior node admittance at allfrequencies, as shown in step 206 of FIG. 2. It uses the KernelPolynomial Method (KPM) which is appropriate for very large sparseHurwitz matrices for lossless circuits.

At step 1101, the Green's Function is expanded in Chebyshev PolynomialMoments. A Chebyshev moment expansion is analogous to a Fourierexpansion.

At step 1102, a finite number of Chebyshev Polynomial Moments of Green'sFunctions are created by Hurwitz matrix on vector multiplication. Thisprovides a truncated moment expansion. The frequency resolution improveswith increasing the number of moments.

At step 1103, truncated Chebyshev Polynomial Moment Expansion of Green'sFunction is evaluated by Fast Fourier Transform. The Kernel in the namecomes from the use of apodization (or reweighting) of the moments in theexpansion to minimize the Gibbs phenomenon.

The details of the KPM are as follows. It is a method developed inquantum physics and chemistry to analyze spectra by evaluating Green'sfunctions. Here KPM is adapted to fast circuit spectral analysis. Forlarge sparse Hurwitz matrices it scales linearly in N for both CPUrequirements and memory requirements. The analogue of the Hurwitz matrixfor Green's function evaluation in quantum systems is a Hamiltonianmatrix, which is Hermitian. KPM is routinely applied to physical systemswhose Hamiltonian matrices have dimension of more than a billion.

KPM uses expansions of the Green's function in a polynomial series. Ifthe eigenspectrum of the Hurwitz matrix is bounded above and below, alleigenvalues may be scaled to the range between −1 to +1. Let a hat Âsymbolize scaling of variables to the range of support of thepolynomial. Then, there is an operator identity for the Dirac deltafunction,

${\delta \left( {\hat{\omega} - \hat{H}} \right)} = {{\frac{1}{\pi \sqrt{1 - {\hat{\omega}}^{2}}}\left\lbrack {1 + {2{\sum\limits_{n = 1}^{\infty}{{T_{n}\left( \hat{\omega} \right)}{T_{n}\left( \hat{H} \right)}}}}} \right\rbrack}.}$

Here, the T_(n) are Chebyshev polynomials of the first kind. Thisidentity may be applied to generate a truncated Chebyshev polynomialexpansion of the imaginary part of the Green's function (or real part ofthe admittance)

${{\overset{\sim}{Y}}_{ea}{\delta \left( {\hat{\omega} - \hat{H}} \right)}{\overset{\sim}{Y}}_{ae}} \approx {\frac{1}{\pi \sqrt{1 - {\hat{\omega}}^{2}}}{\sum\limits_{n = 0}^{N}{g_{n}{T_{n}\left( \hat{\omega} \right)}\mu_{n}\text{;}\mspace{14mu} \mu_{n}}}} \equiv {{\overset{\sim}{Y}}_{ea}{T_{n}\left( \hat{H} \right)}{{\overset{\sim}{Y}}_{ae}.}}$

The real part can be calculated from the imaginary part by theanalyticity relations. The g_(n) are apodization weights used tominimize Gibbs oscillations in truncation of a series. The Chebyshevmoments μ_(n) are calculated by the Hurwitz matrix on vector multipliesusing the Chebyshev polynomial recursion, which is a numerically stableoperation. If the multiplies can be reduced to rules, the storagerequirement may be only three vectors having the dimension of theHurwitz matrix. The number of moments needed scales linearly with theinverse frequency resolution desired. Since Chebyshev polynomialexpansions are truncated Fourier series, evaluation of spectra may beaccomplished by fast Fourier transforms.

If the eigenvalue spectrum is unbounded, then other choices ofpolynomial with infinite or semi-infinite support may be preferred, suchas Hermite, Laguerre and Legendre. KPM can be applied with minimalmodification to the real symmetric Hurwitz matrices appropriate forlarge lossless circuits. The KPM needs further development to beapplicable to lossy circuits.

In addition to the three (diagonalization, linear algebra, kernelpolynomial) methods detailed above, there are other Green's functionevaluation methods widely used in physics that may be adapted to circuitspectral analysis. A Hamiltonian matrix for quantum physics plays asimilar time evolution role as the Hurwitz matrix for circuits. Forexample, a time series of the admittance can be generated by equatingtime evolution as multiplication by the exponential of the timemultiplied by the Hamiltonian, as discussed above for a causal Green'sfunction. This time series may be analyzed by Fourier methods or byFilter Diagonalization methods to calculate the exterior circuitadmittance spectrum. The most appropriate numerical method will dependon the particular circuit being analyzed and the software implementationof the method.

Applications to Spectral Analysis

FIG. 12 is a graph 1200 of S-parameters for a reconfigurable SAW(surface acoustic wave) filter circuit example. The calculations useMATLAB 2016a on one core of a Lenovo laptop. This example is areconfigurable filter project which tunes passives coupled to acousticwave resonators to change the filter response. The model consists of anin-line ladder of eight MBVDs, ten inductors and nine capacitors. EachMBVD contains two capacitors, one inductor and two resistors. As such,the total Hurwitz matrix dimension is 43.

The MBVD parts values are taken from fits to finite element simulationsof the electroacoustic properties of LiTaO₃ SAW resonators, which inturn have been validated by experiment. The Y matrix is sparse. Graph1200 shows three terms of the S matrix (in dB) of the reconfigurable SAWfilter circuit vs. frequency (in MHz) calculated by the algebraic nodetransformation method. S(1,2) is the transfer function from port 1 toport 2 of the reconfigurable SAW filter circuit. S(1,1) is thereflection at port 1 and S(2,2) is the reflection at port 2 of thereconfigurable SAW filter circuit.

FIG. 13 shows a log-log plot 1300 of a number of frequency points versusCPU time for circuit spectral analysis methods. The data indicated witha + symbol correspond to a conventional circuit spectral analysis methodin FIG. 1. It has been mexed in MATLAB (that is, accelerated bycompilation in C++). Its CPU time scales linearly with the number offrequency points. The data with a o symbol labeled H&D correspond to thealgebraic node transformation method using Hurwitz matrixdiagonalization, as shown in FIG. 9. The data with a * symbol labeledH&LA correspond to the fast circuit spectral analysis method using thelinear algebra, as shown in FIG. 10. The data with a ⋄ symbol labeledH&LU also correspond to the fast circuit spectral analysis method usingthe linear algebra, as shown in FIG. 10. The calculation uses LUdecomposition with MATLAB's lu·m function. Neither of these linearalgebra approaches using the Hurwitz matrix are processed with MATLAB'smex function. With large numbers of frequency points, the Hurwitzdiagonalization code is up to 25 times more efficient than theconventional linear algebra code. A typical reconfigurable SAW filterdesign application would require 2000 frequency points to give 0.5 MHzresolution.

FIG. 14 shows a graph 1400 of an example of a Band 2 SAW duplexer Smatrix (in dB) versus frequency (in MHz). The design consists of 29MBVDs connected on a LiTaO₃ piezoelectric substrate with aluminummetallization connecting ports. A Y matrix is modeled by a 57 portelectromagnetic layout calculation. A layout matrix is a distributedpart whose admittance varies slowly with frequency. For the purposes ofcomparing methods, it is taken to be a frequency independent reactance(or FIR). There are also two inductors connecting to exterior ports. TheHurwitz matrix dimension is the sum of three for each MBVD and one foreach inductor for a total of 89. The admittance matrix is dense.

FIG. 15 is a log-log plot 1500 comparing the number of frequency pointsversus CPU time for various circuit spectral analysis methods. The samesymbol labels are used as in FIG. 13. For the number of frequency pointsrequired for typical applications, the algebraic node transformationmethod using Hurwitz matrix diagonalization, shown in FIG. 9, is up to350 times more efficient than the conventional spectral analysismethods. The algebraic node transformation method using the Hurwitzmatrix with linear algebra, shown in FIG. 10, is faster than theconventional spectral analysis method by a factor of about six.

Pole Zero Analysis

‘Analysis’ starts with a circuit and predicts its response. ‘Synthesis’is the inverse process of starting with a desired response and findingcircuits that can reproduce it. For example, the response function maybe a scattering matrix. For each channel, the response F(s) is specifiedas a rational function of frequency s=jω, a ratio of finite ordernumerator polynomial P(s) and equal or higher order denominatorpolynomial Q(s):

$\begin{matrix}{{F(s)} = {{\frac{P(s)}{Q(s)}\text{;}\mspace{14mu} s} = {j\; {\omega.}}}} & (34)\end{matrix}$

In order for the electrical response of the circuit to be stable, thenumerator and denominator polynomials must both be ‘Hurwitz’. This is aset of constraints on the type of polynomials. Monic polynomials may becompletely specified by their roots, so the Hurwitz conditions may berestated in terms of the roots. Roots of numerator polynomials arecalled ‘zeros’. Roots of the denominator polynomial are ‘poles’.

Characterization of a circuit response by its poles and zeros is termed‘pole zero analysis’. This is an important tool for network synthesisand characterization. Ideal components in a ladder topology circuit maybe synthesized by matching poles and zeros to a continued fractionexpansion of the driving point admittance. In principle, for idealcomponents equivalent circuit transformations enable infinite variationsof circuits to be found that meet pole zero objectives.

In practice, real components are lossy and deviate from the ideal model.Realizing the pole zero objectives may be difficult. Real circuits needto be characterized. Real component parameters may need to be tuned,searched and optimized to recover pole zero objectives while maintainingrealizability. The poles and zeros may also need to be optimized tominimize circuit complexity and loss with available components.

Efficient methods for extracting poles and zeros are needed forsynthesis and characterization. The poles and zeros are distributed inthe complex ω-plane. They are found by extending scattering matrixcalculations to complex frequencies. Minima are zeros of numeratorpolynomials and maxima are poles of denominator polynomials. For thesurface acoustic wave device examples considered here, the search coversa two dimensional space of real frequencies over a several hundred MHzrange and imaginary frequencies in the ±100 MHz range. Typical searchesat high resolution require S matrix evaluations at up to a millioncomplex frequencies.

For example, FIG. 16 shows the poles and zeros extracted for the SAWduplexer example. H denotes poles from zeros of the denominator Hurwitzpolynomial in common with all channels. The ports are labeled 1 for Tx,2 for antenna and 3 for Rx. S₁₁ denotes reflection zeros for the Txchannel, S₁₂ denotes transmission zeros between Tx and antenna, S₃₂denotes transmission zeros for the Rx channel, etc. The pole zerostructure is very complex, due to strong electromagnetic cross couplingin the metal on piezoelectric layout connecting the SAW resonators.

Pole zero analysis can be done either using the conventional spectralanalysis method depicted in FIG. 1, or using the new spectral analysismethod shown in FIG. 2. The results are identical. However, the newmethod is approximately two hundred times faster. On a single laptopcore, a pole zero calculation takes less than a second of cpu time. Theconventional method requires minutes. Such orders of magnitude increasein speed enables real time synthesis, in which a change in componentparameters instantaneously changes a display of poles and zeros.

For large numbers of frequency points, these examples demonstrate anorders of magnitude improvement in speed for the algebraic nodetransformation method compared to the conventional spectral analysismethod. The details of the performance comparison between methods willdepend on the specific circuit, the specific software and hardwareimplementation, and the specific needs of the application.

Designing a Filter

FIG. 17 is a flow chart of a process 1700 for designing a microwavefilter. The process 1700 starts at 1705 and ends at 1795 after thedesign of a filter meeting a set of specifications has been completedand a design description has been output. The process 1700 is cyclic innature and all or portions of the process may be repeated iterativelyuntil a successful filter design has been established. While the process1700 is specific to designing a SAW filter, a similar process can beused for designing other types of filters, e.g., BAW, FBAW, TBAW, orMEMS filters.

At 1710, the set of filter specifications is received. Thespecifications may be received, for example, from a potential customeror may be developed by a filter supplier in response to a current orperceived future market need. The filter specifications may include, forexample, specifications on the lower and upper frequencies of a passband and, optionally, one or more stop bands. Filter specifications arecommonly described by limitations on the frequency-dependentS-parameters of the filter. For example, the set of specifications mayinclude a minimum value of S(2,1) (i.e. a maximum insertion loss) overthe pass band and maximum values of S(2,1) (i.e. minimum insertionlosses) over each stop band, if defined. The set of specifications mayinclude a maximum value of S(1,1) (i.e. a minimum return loss) over adefined frequency range. The set of specifications for the filter mayinclude other requirements such as a maximum die size, an operatingtemperature range, an input power level, and other requirements.

At 1720 an architecture for the filter may be defined. Defing the filterarchitecture may include selecting a resonator technology (SAW, BAW,etc.). Assuming that SAW technology is selected, defining the filterarchecture may also include selecting a substrate configuration(single-crystal or composite) and a particular piezoelectric materialand crystalline orientation (i.e. the angle of the surface of asubstrate to the internal crystalline axis). Defining the filterarchitecture may further include selecting a filter topology (ladder,coupled resonator, dual mode SAW, etc.) and the number, type, andarrangement of SAW resonators. Other characteristics of the filter thatmay be defined at 1720 include the type and thickness of the metal usedto form the interdigitated transducers of the SAW resonators, thepresence or absence and material of a dielectric coating over theinterdigitated transducers, a particular package, and other material andmanufacturing-related characteristics.

At 1730, the filter architecture selected at 1720 is converted into aninitial or baseline filter design. The initial filter design assignscharacteristics, such as resonant and anti-resonance frequencies andphysical area to the resonators defined in the filter architecture. Theinitial filter design may be established, for example, by a designengineer, who may be assisted by a commercial, open-source, orproprietary design synthesis software tool. The initial filter designmay be based on the specifications from 1710, the architecture from1720, and experience with previous filter designs. For example, theinitial filter design may be adapted or scaled from a previous designhaving similar requirements.

Alternatively, the baseline filter design may be generated at 1730automatically by the design synthesis software tool based on thespecifications from 1710. In this case, the design synthesis tool mayalso perform portions of defining the filter architecture at 1720 (suchas selecting the number of resonators). In all cases, the initial filterdesign may be represented as an electronic circuit composed ofinterconnected circuit components, with the SAW resonators representedby equivalent circuit models consisting of combinations of lumpedresistive and reactive (i.e. capacitor and inductor) circuit components.

The initial filter design from 1730 is analyzed at 1740 using thespectral analysis methods previously described and shown in FIGS. 2, and9-11. The results of the analysis at 1740 may be an s-parameter matrixor set of frequency dependent s-parameters. At 1750, the analysisresults are compared to the specifications from 1710 to determinewhether or not the filter design meets the specifications. When adetermination is made that the filter design meets the specifications(“yes” at 1750), the final filter design is transformed into a suitabledesign description file that serves as an input to a manufacturingprocess. For example, the design description file can include data forcreating photomasks for use in manufacturing the SAW filter circuit. Theprocess 1700 then ends at 1795.

When a determination is made that the filter design does not meet thespecifications (“no” at 1750), the filter design may be modified at 1760and the modified design may be analyzed at 1740. The actions from 1740to 1750 may be repeated cyclically until a determination is made at 1750that the filter design meets the specifications. During each cycle, themodifications to the filter design at 1760 may be made by the designengineer. Alternatively, the actions at 1740, 1750, and 1760 may beincorporated into a circuit design automation tool that automaticallyoptimizes the performance of the filter design.

In some circumstances (such as after completing a predetermined numberof cycles through the actions from 1740 to 1760), a determination may bemade that the current filter architecture is incapable of meeting thespecifications. In this situation, the process 1700 may return to 1720(as indicated by the dashed arrow 1765) to select a different filterarchitecture. Since each SAW resonator occupies a finite substrate area,a filter with fewer resonators will, in general, use a smaller substrateand cost less to manufacture than a filter with more resonators. Thus apossible sequence of actions may be to initially select, at 1720, afilter architecture with marginally few resonators, and then add one ormore resonators to the architecture each time the process 1700 returnsto 1720. Other changes that may be mae when the process 1700 returns to1720 include different materials or substrate orientation, differentpackaging, different filter topology, and/or different resonatortechnology.

The entire process 1700 may be repeated as necessary until a baselinedesign meeting the specifications is established.

Closing Comments

Throughout this description, the embodiments and examples shown shouldbe considered as exemplars, rather than limitations on the apparatus andprocedures disclosed or claimed. Although many of the examples presentedherein involve specific combinations of method acts or system elements,it should be understood that those acts and those elements may becombined in other ways to accomplish the same objectives. With regard toflowcharts, additional and fewer steps may be taken, and the steps asshown may be combined or further refined to achieve the methodsdescribed herein. Acts, elements and features discussed only inconnection with one embodiment are not intended to be excluded from asimilar role in other embodiments.

As used herein, “plurality” means two or more. As used herein, a “set”of items may include one or more of such items. As used herein, whetherin the written description or the claims, the terms “comprising”,“including”, “carrying”, “having”, “containing”, “involving”, and thelike are to be understood to be open-ended, i.e., to mean including butnot limited to. Only the transitional phrases “consisting of” and“consisting essentially of”, respectively, are closed or semi-closedtransitional phrases with respect to claims. Use of ordinal terms suchas “first”, “second”, “third”, etc., in the claims to modify a claimelement does not by itself connote any priority, precedence, or order ofone claim element over another or the temporal order in which acts of amethod are performed, but are used merely as labels to distinguish oneclaim element having a certain name from another element having a samename (but for use of the ordinal term) to distinguish the claimelements. As used herein, “and/or” means that the listed items arealternatives, but the alternatives also include any combination of thelisted items.

It is claimed:
 1. A method, comprising: iteratively modifying amicrowave filter design, by a computing system, until a circuitscattering matrix of the microwave filter design over a predeterminedfrequency range corresponds to a set of specifications, the microwavefilter design including a plurality of resonators, each resonatorrepresented by a respective equivalent circuit model comprisingresistive and reactive circuit components; and generating, by thecomputing system, a filter design description file for input to amanufacturing process.
 2. The method of claim 1, wherein the circuitscattering matrix is generated from a circuit exterior node admittancematrix of the microwave filter design over the predetermined frequencyrange.
 3. The method of claim 2, wherein the circuit exterior nodeadmittance matrix is generated via a Green's Function from a circuitadmittance matrix for exterior nodes and algebraic nodes of themicrowave filter design.
 4. The method of claim 3, further comprising:calculating a part admittance matrix with algebraic nodes for eachcircuit component; and calculating the circuit admittance matrix basedon the part admittance matrices.
 5. The method of claim 4, whereincalculating the circuit admittance matrix further comprises reducinginterior nodes of the circuit admittance matrix.
 6. The method of claim3, further comprising reducing the algebraic nodes of the circuitadmittance matrix.
 7. The method of claim 6, further comprising:calculating a time evolution of exterior node to algebraic nodeadmittance using an exponential of a Hurwitz matrix; and filteringdiagonalization of the time evolution to generate the circuit exteriornode admittance matrix.
 8. The method of claim 3, further comprisingevaluating the Green's Function by linear algebra one frequency at atime over the predetermined frequency range to determine the circuitexterior node admittance matrix.
 9. The method of claim 1, wherein eachresonator of the plurality of resonators is a surface acoustic waveresonator, a bulk acoustic wave resonator, a film bulk acoustic waveresonator, a thin-film bulk acoustic wave resonator, or amicro-electro-mechanical system resonator.
 10. The method of claim 1,wherein each resonator of the plurality of resonators is a surfaceacoustic wave resonator.
 11. A system, comprising: a computing systemcomprising at least one processor configured to: iteratively modify amicrowave filter design until a circuit scattering matrix of themicrowave filter design over a predetermined frequency range correspondsto a set of specifications, the microwave filter design including aplurality of resonators, each resonator represented by a respectiveequivalent circuit model comprising resistive and reactive circuitcomponents; and generate a filter design description file for input to amanufacturing process.
 12. The system of claim 11, wherein the circuitscattering matrix is generated from a circuit exterior node admittancematrix of the microwave filter design over the predetermined frequencyrange.
 13. The system of claim 12, wherein the circuit exterior nodeadmittance matrix is generated via a Green's Function from a circuitadmittance matrix for exterior nodes and algebraic nodes of themicrowave filter design.
 14. The system of claim 13, wherein theprocessor is further configured to: calculate a part admittance matrixwith algebraic nodes for each circuit component; and calculate thecircuit admittance matrix based on the part admittance matrices.
 15. Thesystem of claim 14, wherein the processor is further configured toreduce interior nodes of the circuit admittance matrix.
 16. The systemof claim 13, wherein the processor is further configured to reduce thealgebraic nodes of the circuit admittance matrix.
 17. The system ofclaim 16, wherein the processor is further configured to: calculate atime evolution of exterior node to algebraic node admittance using anexponential of a Hurwitz matrix; and filter diagonalization of the timeevolution to generate the circuit exterior node admittance matrix. 18.The system of claim 13, wherein the processor is further configured toevaluate the Green's Function by linear algebra one frequency at a timeover the predetermined frequency range to determine the circuit exteriornode admittance matrix.
 19. The system of claim 11, wherein eachresonator of the plurality of resonators is a surface acoustic waveresonator, a bulk acoustic wave resonator, a film bulk acoustic waveresonator, a thin-film bulk acoustic wave resonator, or amicro-electro-mechanical system resonator.
 20. The system of claim 11,wherein each resonator of the plurality of resonators is a surfaceacoustic wave resonator.